code: def initialize_scale_zp(
      self, wrapper: ModuleWrapper, target_name: str, target: Tensor
  ):
      if self.is_init or not self.check_target(wrapper, target_name):
          return
      target_space = wrapper.quantization_target_spaces[target_name]
      if target_space.type is TargetType.INPUT:
          return
      elif target_space.type in [
          TargetType.OUTPUT,
          TargetType.PARAMETER,
      ]:
          tracked_max = torch.tensor(1.0).to(target.device)
          tracked_min = torch.tensor(0.0).to(target.device)
          scale, zero_point = init_scale_zp(
              tracked_max,
              tracked_min,
              target_space.qmax,
              target_space.qmin,
              "affine",
          )
      else:
          raise RuntimeError(f"Unknown target_name {target_name}")
  
      target_space.scale, target_space.zero_point = scale, zero_point
  
imports: from __future__ import annotations
  import logging
  from typing import List, Dict, Union, overload
  import torch
  import torch.nn
  from torch import Tensor
  from nni.common.version import torch_version_is_2
  from ..base.compressor import Compressor, Quantizer
  from ..base.wrapper import ModuleWrapper
  from ..utils import Evaluator, _EVALUATOR_DOCSTRING
  from ..base.target_space import TargetType, QuantizationTargetSpace
label_desc: Use image node_id 11 for calling the DoReFaQuantizer obj's underlying member method code with example usage: obj.initialize_scale_zp(wrapper, target_name, target) without return types